Yiran Chen develops brain-inspired semiconductor hardware to enable faster, greener AI at the edge.
Neuromorphic computing, inspired by the brain, integrates memory and processing to drastically reduce power consumption compared to traditional CPUs and GPUs, making AI at the network edge more ...
Nanotechnology market continues to surge in prominence, driven by advancements in nanotubes and nanocomposites. This growth is fueled by increased adoption in electronics, healthcare, and automotive ...
Scientists demonstrate neuromorphic computing utilizing perovskite microcavity exciton polaritons operating at room temperature. (Nanowerk News) Neuromorphic computing, inspired by the human brain, is ...
Zinc doping enables visible-light programming of ferroelectric memristors for neuromorphic computing
Adding zinc ions to lithium niobate crystals cuts the energy needed for polarization switching by 69%, enabling visible-light programming of memristors for brain-inspired computing.
Brain-like AI computers demonstrate strong math capabilities, reshaping hardware design with enhanced energy efficiency and ...
BANGALORE, India, Jan. 28, 2026 /PRNewswire/ -- According to Valuates Reports, In 2024, the global market size of Neuromorphic AI Semiconductor was estimated to be worth USD 30.5 Million and is ...
Large scale datasets and information processing requirements, within complex environments, are continuously reaching unprecedented levels of sophistication, especially in the advent of artificial ...
Neuromorphic computing aims to replicate the functional architecture of the human brain by integrating electronic components that mimic synaptic and neuronal behaviours. Central to this endeavour are ...
Los Alamos National Laboratory Researchers Design New Artificial Synapses for Neuromorphic Computing
Tested against a dataset of handwritten images from the Modified National Standards and Technology database, the interface-type memristors realized a high image recognition accuracy of 94.72%. (Los ...
This review describes various types of low-power memristors, demonstrating their potential for a wide range of applications. This review summarizes low-power memristors for multi-level storage, ...
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